Advertisement

An empirical evaluation of extreme learning machine: application to handwritten character recognition

  • Dibyasundar Das
  • Deepak Ranjan NayakEmail author
  • Ratnakar Dash
  • Banshidahar Majhi
Article

Abstract

Extreme learning machine (ELM), a randomized learning paradigm for single hidden layer feed-forward network, has gained significant attention for solving problems in diverse domains due to its faster learning ability. The output weights in ELM are determined by an analytic procedure, while the input weights and biases are randomly generated and fixed during the training phase. The learning performance of ELM is highly sensitive to many factors such as the number of nodes in the hidden layer, the initialization of input weight and the type of activation functions in the hidden layer. Although various works on ELM have been proposed in the last decade, the effect of the all these influencing factors on classification performance has not been fully investigated yet. In this paper, we test the performance of ELM with different configurations through an empirical evaluation on three standard handwritten character datasets, namely, MNIST, ISI-Kolkata Bangla numeral, ISI-Kolkata Odia numeral and a newly developed NIT-RKL Bangla numeral dataset. Finally, we derive some best ELM figurations which can serve as general guidelines to design ELM based classifiers.

Keywords

Extreme learning machine Weight initialization Activation function Character recognition 

Notes

References

  1. 1.
    Basu S, Das N, Sarkar R, Kundu M, Nasipuri M, Basu DK (2010) A novel framework for automatic sorting of postal documents with multi-script address blocks. Pattern Recogn 43(10):3507–3521CrossRefGoogle Scholar
  2. 2.
    Bhalerao M, Bonde S, Nandedkar A, Pilawan S (2018) Combined classifier approach for offline handwritten Devanagari character recognition using multiple features. In: Computational vision and bio inspired computing. Springer, pp 45–54Google Scholar
  3. 3.
    Bhattacharya U, Chaudhuri B (2005) Databases for research on recognition of handwritten characters of Indian scripts. In: Eighth International conference on document analysis and recognition, 2005. Proceedings. IEEE, pp 789–793Google Scholar
  4. 4.
    Bhattacharya U, Chaudhuri BB (2009) Handwritten numeral databases of Indian scripts and multistage recognition of mixed numerals. IEEE Trans Pattern Anal Mach Intell 31(3):444–457CrossRefGoogle Scholar
  5. 5.
    Bhowmik TK, Parui SK, Bhattacharya U, Shaw B (2006) An HMM based recognition scheme for handwritten Oriya numerals. In: International conference on information technology IEEE, pp 105–110.Google Scholar
  6. 6.
    Broomhead DS, Lowe D (1988) Radial basis functions, multi-variable functional interpolation and adaptive networks. Tech. rep, Royal Signals and Radar Establishment Malvern (United Kingdom)Google Scholar
  7. 7.
    Cecotti H (2016) Deep random vector functional link network for handwritten character recognition. In: 2016 International joint conference on neural networks (IJCNN). IEEE, pp 3628–3633Google Scholar
  8. 8.
    Cireşan DC, Meier U, Gambardella LM, Schmidhuber J (2010) Deep, big, simple neural nets for handwritten digit recognition. Neural Comput 22(12):3207–3220CrossRefGoogle Scholar
  9. 9.
    Cui D, Huang GB, Liu T (2018) ELM based smile detection using distance vector. Pattern Recogn 79:356–369CrossRefGoogle Scholar
  10. 10.
    Dash KS, Puhan N, Panda G (2014) A hybrid feature and discriminant classifier for high accuracy handwritten Odia numeral recognition. In: IEEE Region 10 symposium. IEEE, pp 531–535Google Scholar
  11. 11.
    Dash KS, Puhan N, Panda G (2014) Non-redundant stockwell transform based feature extraction for handwritten digit recognition. In: International conference on signal processing and communications. IEEE, pp 1–4Google Scholar
  12. 12.
    Dash KS, Puhan N, Panda G (2015) On extraction of features for handwritten Odia numeral recognition in transformed domain. In: Eighth International conference on advances in pattern recognition. IEEE, pp 1–6Google Scholar
  13. 13.
    Eshtay M, Faris H, Obeid N (2018) Improving extreme learning machine by competitive swarm optimization and its application for medical diagnosis problems. Expert Systems with ApplicationsGoogle Scholar
  14. 14.
    Ghosh D, Dube T, Shivaprasad A (2010) Script recognition—a review. IEEE Trans Pattern Anal Mach Intell 32(12):2142–2161CrossRefGoogle Scholar
  15. 15.
    Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the thirteenth international conference on artificial intelligence and statistics, pp 249–256Google Scholar
  16. 16.
    He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778Google Scholar
  17. 17.
    Huang GB, Siew CK (2004) Extreme learning machine: RBF network case. In: Control, automation, robotics and vision conference, vol 2. IEEE, pp 1029–1036Google Scholar
  18. 18.
    Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501CrossRefGoogle Scholar
  19. 19.
    Huang GB, Chen L, Siew CK, et al. (2006) Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans Neural Netw 17(4):879–892CrossRefGoogle Scholar
  20. 20.
    Huang GB, Wang D, Lan Y (2011) Extreme learning machines: a survey. Int J Mach Learn Cybern 2(2):107–122CrossRefGoogle Scholar
  21. 21.
    Huang GB, Zhou H, Ding X, Zhang R (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B (Cybern) 42(2):513–529CrossRefGoogle Scholar
  22. 22.
    Kasun LLC, Yang Y, Huang GB, Zhang Z (2016) Dimension reduction with extreme learning machine. IEEE Trans Image Process 25(8):3906–3918MathSciNetCrossRefGoogle Scholar
  23. 23.
    Kégl B, Busa-Fekete R (2009) Boosting products of base classifiers. In: Proceedings of the 26th annual international conference on machine learning. ACM, pp 497–504Google Scholar
  24. 24.
    Keysers D, Deselaers T, Gollan C, Ney H (2007) Deformation models for image recognition. IEEE Trans Pattern Anal Mach Intell 29(8):1422–1435CrossRefGoogle Scholar
  25. 25.
    Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324CrossRefGoogle Scholar
  26. 26.
    Liang NY, Huang GB, Saratchandran P, Sundararajan N (2006) A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Trans Neural Netw 17(6):1411–1423CrossRefGoogle Scholar
  27. 27.
    Liu CL, Suen CY (2009) A new benchmark on the recognition of handwritten Bangla and Farsi numeral characters. Pattern Recogn 42(12):3287–3295CrossRefGoogle Scholar
  28. 28.
    Liu CL, Nakashima K, Sako H, Fujisawa H (2003) Handwritten digit recognition: benchmarking of state-of-the-art techniques. Pattern Recogn 36 (10):2271–2285CrossRefGoogle Scholar
  29. 29.
    Liu T, Lekamalage CKL, Huang GB, Lin Z (2018) Extreme learning machine for joint embedding and clustering. Neurocomputing 277:78–88CrossRefGoogle Scholar
  30. 30.
    Mahto MK, Kumari A, Panigrahi S (2011) A system for Oriya handwritten numeral recognition for Indian postal automation. Int J Appl Sci Technol Res Excell 1 (1):17–23Google Scholar
  31. 31.
    Mishra TK, Majhi B, Panda S (2013) A comparative analysis of image transformations for handwritten Odia numeral recognition. In: International conference on advances in computing, communications and informatics. IEEE, pp 790–793Google Scholar
  32. 32.
    Mishra TK, Majhi B, Sa PK, Panda S (2014) Model based Odia numeral recognition using fuzzy aggregated features. Front Comput Sci 8(6):916–922MathSciNetCrossRefGoogle Scholar
  33. 33.
    Mohammed AA, Minhas R, Wu QJ, Sid-Ahmed MA (2011) Human face recognition based on multidimensional pca and extreme learning machine. Pattern Recogn 44(10-11):2588–2597CrossRefGoogle Scholar
  34. 34.
    Mohapatra RK, Majhi B, Jena SK (2015) Classification performance analysis of mnist dataset utilizing a multi-resolution technique. In: International conference on computing, communication and security (ICCCS), 2015. IEEE, pp 1–5Google Scholar
  35. 35.
    Mori S, Suen CY, Yamamoto K (1995) Historical review of OCR research and development. In: Document image analysis. IEEE Computer Society Press, pp 244–273Google Scholar
  36. 36.
    Nayak DR, Dash R, Majhi B (2017) Development of pathological brain detection system using jaya optimized improved extreme learning machine and orthogonal ripplet-ii transform. Multimed Tools Appl, 1–29Google Scholar
  37. 37.
    Nayak DR, Dash R, Majhi B (2018) Discrete ripplet-ii transform and modified PSO based improved evolutionary extreme learning machine for pathological brain detection. Neurocomputing 282:232–247CrossRefGoogle Scholar
  38. 38.
    Pan C, Park DS, Yang Y, Yoo HM (2012) Leukocyte image segmentation by visual attention and extreme learning machine. Neural Comput and Applic 21 (6):1217–1227CrossRefGoogle Scholar
  39. 39.
    Plamondon R, Srihari SN (2000) Online and off-line handwriting recognition: a comprehensive survey. IEEE Trans Pattern Anal Mach Intell 22(1):63–84CrossRefGoogle Scholar
  40. 40.
    Sarangi PK, Ahmed P, Ravulakollu KK (2014) Naïve bayes classifier with lu factorization for recognition of handwritten Odia numerals. Indian J Sci Technol 7 (1):35–38Google Scholar
  41. 41.
    Sethy A, Patra PK, Nayak DR (2018) Gray-level co-occurrence matrix and random forest based off-line Odia handwritten character recognition. Recent Patents on EngineeringGoogle Scholar
  42. 42.
    Sethy A, Patra PK, Nayak DR (2018) Off-line handwritten Odia character recognition using DWT and PCA. In: Progress in advanced computing and intelligent engineering. Springer, pp 187–195Google Scholar
  43. 43.
    Song Y, He B, Zhao Y, Li G, Sha Q, Shen Y, Yan T, Nian R, Lendasse A (2018) Segmentation of sidescan sonar imagery using markov random fields and extreme learning machine. IEEE Journal of Oceanic EngineeringGoogle Scholar
  44. 44.
    Tang B, Liu X, Lei J, Song M, Tao D, Sun S, Dong F (2016) Deepchart: combining deep convolutional networks and deep belief networks in chart classification. Signal Process 124:156–161CrossRefGoogle Scholar
  45. 45.
    Tao D, Lin X, Jin L, Li X (2016) Principal component 2-D long short-term memory for font recognition on single Chinese characters. IEEE Trans Cybern 46 (3):756–765CrossRefGoogle Scholar
  46. 46.
    Tao D, Guo Y, Li Y, Gao X (2018) Tensor rank preserving discriminant analysis for facial recognition. IEEE Trans Image Process 27(1):325–334MathSciNetCrossRefGoogle Scholar
  47. 47.
    Wang D (2016) Editorial: randomized algorithms for training neural networks. Inform Sci 364–365:126–128CrossRefGoogle Scholar
  48. 48.
    Wen Y, He L (2012) A classifier for Bangla handwritten numeral recognition. Expert Syst Appl 39(1):948–953CrossRefGoogle Scholar
  49. 49.
    Wen Y, Lu Y, Shi P (2007) Handwritten Bangla numeral recognition system and its application to postal automation. Pattern Recogn 40(1):99–107CrossRefGoogle Scholar
  50. 50.
    Wen X, Liu H, Yan G, Sun F (2018) Weakly paired multimodal fusion using multilayer extreme learning machine. Soft Comput 22(11):3533–3544CrossRefGoogle Scholar
  51. 51.
    Weng Q, Mao Z, Lin J, Liao X (2018) Land-use scene classification based on a CNN using a constrained extreme learning machine. Int J Remote Sens, 1–19Google Scholar
  52. 52.
    Xie W, Li Y, Ma Y (2016) Breast mass classification in digital mammography based on extreme learning machine. Neurocomputing 173:930–941CrossRefGoogle Scholar
  53. 53.
    Xu Y, Shu Y (2006) Evolutionary extreme learning machine–based on particle swarm optimization. In: International symposium on neural networks. Springer, pp 644–652Google Scholar
  54. 54.
    Zeng N, Zhang H, Liu W, Liang J, Alsaadi FE (2017) A switching delayed PSO optimized extreme learning machine for short-term load forecasting. Neurocomputing 240:175–182CrossRefGoogle Scholar
  55. 55.
    Zhang YD, Zhao G, Sun J, Wu X, Wang ZH, Liu HM, Govindaraj VV, Zhan T, Li J (2017) Smart pathological brain detection by synthetic minority oversampling technique, extreme learning machine, and jaya algorithm. Multimed Tools Appl, 1–20Google Scholar
  56. 56.
    Zou W, Yao F, Zhang B, Guan Z (2018) Back propagation convex extreme learning machine. In: Proceedings of ELM-2016. Springer, pp 259–272Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Pattern Recognition Lab, Department of Computer Science and EngineeringNational Institute of TechnologyRourkelaIndia

Personalised recommendations